Research on High Frequency Multi-Source New Energy Area Power Prediction Technology Under Complex Subsurface

The current method of regional power prediction based on multi-source data reduces the dimensionality of multiple factors affecting power by principal component analysis, so as to achieve the prediction of regional power, which leads to poor accuracy of prediction results due to the lack of detailed division of regions. In this regard, the high-frequency multi-source new energy regional power prediction technique under complex subsurface is proposed. The K-means clustering algorithm is used to divide the prediction sub-region, and the benchmark wind farm is selected to establish the regional power prediction model. In the experiment, the prediction accuracy of the proposed method is verified. The analysis of the experimental results shows that the proposed method has a high degree of fit between the prediction results and the actual values when predicting the power of high-frequency multi-source new energy areas under complex substrates, and has a better prediction accuracy.